Assessing Lake Ecosystem Recovery from Acidification and Responses to Emerging Environmental Stressors: a Paleolimnological Perspective
Bibliographic record
Abstract
Mining and smelting activities have heavily affected the Sudbury (Ontario, Canada) region since the late-19th century, leading to acidification and metal contamination in many ecosystems. Regulations on restricting acidic emissions were enacted in the 1970s, after which many Sudbury-region lakes recorded increasing pH and decreasing metal concentrations. Meanwhile, other emerging stressors have likely affected these lakes over the past few decades. Here, I revisit lakes in the Sudbury region, half a century after the application of remedial efforts, to assess lake ecosystem recovery from acidification and their responses to newly emerging environmental stressors. First, a canonical correspondence analysis is used to assess the relationships between surface-sediment diatom assemblage structure and environmental variables for 80 lakes. Lakewater pH remains the strongest environmental variable shaping diatom species distributions, and so is used to construct a robust inference model (R2boot = 0.73; RMSEP = 0.32). Additionally, by assessing ecological changes experienced by a subset of these lakes (n = 33, in common with Dixit et al. 2002) over the past few decades, two major trends are identified: an overall increase in diatom-inferred pH and a rise in the relative abundance of planktonic taxa. Further, down-core analyses in dated sediment cores are conducted to assess detailed ecological changes for three historically acidified lakes and two reference systems over the past ~200 years. Despite recording marked chemical recovery, the acidified lakes showed minimal or no evidence of biological recovery, with recent diatom assemblages markedly different from pre-disturbance assemblages, likely due to the legacy effects of acidification and climate warming. Biological recovery is lagging chemical recovery in acidified systems half a century after the reduction of acid deposition, and a return to pre-disturbance biological assemblages may never be achieved due to emerging environmental stressors, especially recently accelerated climate warming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".